KerNL: Kernel-Based Nonlinear Approach to Parallel MRI Reconstruction.

ABSTRACT: The conventional calibration-based parallel imaging method assumes a linear relationship between the acquired multi-channel k-space data and the unacquired missing data, where the linear coefficients are estimated using some auto-calibration data. In this paper, we first analyze the model errors in the conventional calibration-based methods and demonstrate the nonlinear relationship. Then, a much more general nonlinear framework is proposed for auto-calibrated parallel imaging. In this framework, kernel tricks are employed to represent the general nonlinear relationship between acquired and unacquired k-space data without increasing the computational complexity. Identification of the nonlinear relationship is still performed by solving linear equations. Experimental results demonstrate tha

SUBMITTER: Lyu J 

PROVIDER: S-EPMC6422679 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature